Modelling the impact of hybrid immunity on future COVID-19 epidemic waves

BACKGROUND

Since the emergence of SARS-CoV-2 (COVID-19), there have been multiple waves of infection and multiple rounds of vaccination rollouts. Both prior infection and vaccination can prevent future infection and reduce severity of outcomes, combining to form hybrid immunity against COVID-19 at the individual and population level. Here, we explore how different combinations of hybrid immunity affect the size and severity of near-future Omicron waves.

METHODS

To investigate the role of hybrid immunity, we use an agent-based model of COVID-19 transmission with waning immunity to simulate outbreaks in populations with varied past attack rates and past vaccine coverages, basing the demographics and past histories on the World Health Organization Western Pacific Region.

RESULTS

We find that if the past infection immunity is high but vaccination levels are low, then the secondary outbreak with the same variant can occur within a few months after the first outbreak; meanwhile, high vaccination levels can suppress near-term outbreaks and delay the second wave. Additionally, hybrid immunity has limited impact on future COVID-19 waves with immune-escape variants.

CONCLUSIONS

Enhanced understanding of the interplay between infection and vaccine exposure can aid anticipation of future epidemic activity due to current and emergent variants, including the likely impact of responsive vaccine interventions.

COVID-19 vaccine coverage targets to inform reopening plans in a low incidence setting

Since the emergence of SARS-CoV-2 in 2019 through to mid-2021, much of the Australian population lived in a COVID-19-free environment. This followed the broadly successful implementation of a strong suppression strategy, including international border closures. With the availability of COVID-19 vaccines in early 2021, the national government sought to transition from a state of minimal incidence and strong suppression activities to one of high vaccine coverage and reduced restrictions but with still-manageable transmission. This transition is articulated in the national 're-opening' plan released in July 2021. Here, we report on the dynamic modelling study that directly informed policies within the national re-opening plan including the identification of priority age groups for vaccination, target vaccine coverage thresholds and the anticipated requirements for continued public health measures-assuming circulation of the Delta SARS-CoV-2 variant. Our findings demonstrated that adult vaccine coverage needed to be at least 60% to minimize public health and clinical impacts following the establishment of community transmission. They also supported the need for continued application of test-trace-isolate-quarantine and social measures during the vaccine roll-out phase and beyond.

The incidence of nosocomial bloodstream infection and urinary tract infection in Australian hospitals before and during the COVID-19 pandemic: an interrupted time series study

BACKGROUND

The COVID-19 pandemic has had a significant impact on healthcare including increased awareness of infection prevention and control (IPC). The aim of this study was to explore if the heightened awareness of IPC measures implemented in response to the pandemic influenced the rates of healthcare associated infections (HAI) using positive bloodstream and urine cultures as a proxy measure.

METHODS

A 3 year retrospective review of laboratory data from 5 hospitals (4 acute public, 1 private) from two states in Australia was undertaken. Monthly positive bloodstream culture data and urinary culture data were collected from January 2017 to March 2021. Occupied bed days (OBDs) were used to generate monthly HAI incidence per 10,000 OBDs. An interrupted time series analysis was undertaken to compare incidence pre and post February 2020 (the pre COVID-19 cohort and the COVID-19 cohort respectively). A HAI was assumed if positive cultures were obtained 48 h after admission and met other criteria.

RESULTS

A total of 1,988 bloodstream and 7,697 urine positive cultures were identified. The unadjusted incident rate was 25.5 /10,000 OBDs in the pre-COVID-19 cohort, and 25.1/10,000 OBDs in the COVID-19 cohort. The overall rate of HAI aggregated for all sites did not differ significantly between the two periods. The two hospitals in one state which experienced an earlier and larger outbreak demonstrated a significant downward trend in the COVID-19 cohort (p = 0.011).

CONCLUSION

These mixed findings reflect the uncertainty of the effect the pandemic has had on HAI's. Factors to consider in this analysis include local epidemiology, differences between public and private sector facilities, changes in patient populations and profiles between hospitals, and timing of enhanced IPC interventions. Future studies which factor in these differences may provide further insight on the effect of COVID-19 on HAIs.

COVID-19 in low-tolerance border quarantine systems: Impact of the Delta variant of SARS-CoV-2

In controlling transmission of coronavirus disease 2019 (COVID-19), the effectiveness of border quarantine strategies is a key concern for jurisdictions in which the local prevalence of disease and immunity is low. In settings like this such as China, Australia, and New Zealand, rare outbreak events can lead to escalating epidemics and trigger the imposition of large-scale lockdown policies. Here, we develop and apply an individual-based model of COVID-19 to simulate case importation from managed quarantine under various vaccination scenarios. We then use the output of the individual-based model as input to a branching process model to assess community transmission risk. For parameters corresponding to the Delta variant, our results demonstrate that vaccination effectively counteracts the pathogen's increased infectiousness. To prevent outbreaks, heightened vaccination in border quarantine systems must be combined with mass vaccination. The ultimate success of these programs will depend sensitively on the efficacy of vaccines against viral transmission.

Understanding how Victoria, Australia gained control of its second COVID-19 wave

During 2020, Victoria was the Australian state hardest hit by COVID-19, but was successful in controlling its second wave through aggressive policy interventions. We calibrated a detailed compartmental model of Victoria's second wave to multiple geographically-structured epidemic time-series indicators. We achieved a good fit overall and for individual health services through a combination of time-varying processes, including case detection, population mobility, school closures, physical distancing and face covering usage. Estimates of the risk of death in those aged ≥75 and of hospitalisation were higher than international estimates, reflecting concentration of cases in high-risk settings. We estimated significant effects for each of the calibrated time-varying processes, with estimates for the individual-level effect of physical distancing of 37.4% (95%CrI 7.2-56.4%) and of face coverings of 45.9% (95%CrI 32.9-55.6%). That the multi-faceted interventions led to the dramatic reversal in the epidemic trajectory is supported by our results, with face coverings likely particularly important.

Bringing evidence from press release to the clinic in the era of COVID-19

The urgent need to develop effective therapeutics and disseminate information from clinical studies has led to data from clinical trials being made available by alternate methods prior to peer-reviewed publication, including press releases, social media and pre-print papers. While this allows clinicians more open access to these data, a trust has to be placed with the investigators releasing these data without the availability of scientifically rigorous peer review. The examples of results from trials studying dexamethasone and hydroxychloroquine for treatment of COVID-19 have had contrasting outcomes, including the potential for significant numbers of lives saved with the early release of results from the RECOVERY trial studying dexamethasone contrasting with unsubstantiated data being presented from trials studying hydroxychloroquine. Clinicians and researchers must maintain a healthy scepticism when reviewing results prior to peer-reviewed publication, but also consider when these opportunities may allow for early implementation of potentially lifesaving interventions for people infected with COVID-19.

Risk mapping for COVID-19 outbreaks in Australia using mobility data

COVID-19 is highly transmissible and containing outbreaks requires a rapid and effective response. Because infection may be spread by people who are pre-symptomatic or asymptomatic, substantial undetected transmission is likely to occur before clinical cases are diagnosed. Thus, when outbreaks occur there is a need to anticipate which populations and locations are at heightened risk of exposure. In this work, we evaluate the utility of aggregate human mobility data for estimating the geographical distribution of transmission risk. We present a simple procedure for producing spatial transmission risk assessments from near-real-time population mobility data. We validate our estimates against three well-documented COVID-19 outbreaks in Australia. Two of these were well-defined transmission clusters and one was a community transmission scenario. Our results indicate that mobility data can be a good predictor of geographical patterns of exposure risk from transmission centres, particularly in outbreaks involving workplaces or other environments associated with habitual travel patterns. For community transmission scenarios, our results demonstrate that mobility data add the most value to risk predictions when case counts are low and spatially clustered. Our method could assist health systems in the allocation of testing resources, and potentially guide the implementation of geographically targeted restrictions on movement and social interaction.

Estimation of the force of infection and infectious period of skin sores in remote Australian communities using interval-censored data

Prevalence of impetigo (skin sores) remains high in remote Australian Aboriginal communities, Fiji, and other areas of socio-economic disadvantage. Skin sore infections, driven primarily in these settings by Group A Streptococcus (GAS) contribute substantially to the disease burden in these areas. Despite this, estimates for the force of infection, infectious period and basic reproductive ratio-all necessary for the construction of dynamic transmission models-have not been obtained. By utilising three datasets each containing longitudinal infection information on individuals, we estimate each of these epidemiologically important parameters. With an eye to future study design, we also quantify the optimal sampling intervals for obtaining information about these parameters. We verify the estimation method through a simulation estimation study, and test each dataset to ensure suitability to the estimation method. We find that the force of infection differs by population prevalence, and the infectious period is estimated to be between 12 and 20 days. We also find that optimal sampling interval depends on setting, with an optimal sampling interval between 9 and 11 days in a high prevalence setting, and 21 and 27 days for a lower prevalence setting. These estimates unlock future model-based investigations on the transmission dynamics of skin sores.

High burden of infectious disease and antibiotic use in early life in Australian Aboriginal communities

OBJECTIVE

To quantify the childhood infectious disease burden and antibiotic use in the Northern Territory's East Arnhem region through synthesis and analysis of historical data resources.

METHODS

We combined primary health clinic data originally reported in three separate publications stemming from the East Arnhem Healthy Skin Project (Jan-01 to Sep-07). Common statistical techniques were used to explore the prevalence of infectious conditions and the seasonality of infections, and to measure rates of antibiotic use.

RESULTS

There was a high monthly prevalence of respiratory (mean: 32% [95% confidence interval (CI): 20%, 34%]) and skin (mean: 20% [95%CI: 19%, 22%]) infectious syndromes, with upper respiratory tract infections (mean: 29% [95%CI: 27%, 31%]) and skin sores (mean: 15% [95%CI: 14%, 17%]) the most common conditions. Antibiotics were frequently prescribed with 95% (95%CI: 91%, 97%) of children having received at least one antibiotic prescription by their first birthday, and 47% having received six antibiotic prescriptions; skin sores being a key driver.

CONCLUSIONS

Early life infections drive high antibiotic prescribing rates in remote Aboriginal communities. Implications for public health: Eliminating skin disease could reduce antibiotic use by almost 20% in children under five years of age in this population.

A biological model of scabies infection dynamics and treatment informs mass drug administration strategies to increase the likelihood of elimination

Infections with Sarcoptes scabiei, or scabies, remain common in many disadvantaged populations. Mass drug administration (MDA) has been used in such settings to achieve a rapid reduction in infection and transmission, with the goal of eliminating the public health burden of scabies. While prevalence has been observed to fall substantially following such an intervention, in some instances resurgence of infection to baseline levels has occurred over several years. To explore the biology underpinning this phenomenon, we have developed a theoretical model of scabies life-cycle and transmission dynamics in a homogeneously mixing population, and simulate the impact of mass drug treatment strategies acting on egg and mite life cycle stages (ovicidal) or mites alone (non-ovicidal). In order to investigate the dynamics of the system, we first define and calculate the optimal interval between treatment doses. We calculate the probability of eradication as a function of the number of optimally-timed successive treatment doses and the number of years over which a program is run. For the non-ovicidal intervention, we first show that at least two optimally-timed doses are required to achieve eradication. We then demonstrate that while more doses over a small number of years provides the highest chance of eradication, a similar outcome can be achieved with fewer doses delivered annually over a longer period of time. For the ovicidal intervention, we find that doses should be delivered as close together as possible. This work provides a platform for further research into optimal treatment strategies which may incorporate heterogeneity of transmission, and the interplay between MDA and enhancement of continuing scabies surveillance and treatment strategies.

Indigenous Australian household structure: a simple data collection tool and implications for close contact transmission of communicable diseases

Households are an important location for the transmission of communicable diseases. Social contact between household members is typically more frequent, of greater intensity, and is more likely to involve people of different age groups than contact occurring in the general community. Understanding household structure in different populations is therefore fundamental to explaining patterns of disease transmission in these populations. Indigenous populations in Australia tend to live in larger households than non-Indigenous populations, but limited data are available on the structure of these households, and how they differ between remote and urban communities. We have developed a novel approach to the collection of household structure data, suitable for use in a variety of contexts, which provides a detailed view of age, gender, and room occupancy patterns in remote and urban Australian Indigenous households. Here we report analysis of data collected using this tool, which quantifies the extent of crowding in Indigenous households, particularly in remote areas. We use these data to generate matrices of age-specific contact rates, as used by mathematical models of infectious disease transmission. To demonstrate the impact of household structure, we use a mathematical model to simulate an influenza-like illness in different populations. Our simulations suggest that outbreaks in remote populations are likely to spread more rapidly and to a greater extent than outbreaks in non-Indigenous populations.